def PWC_full(B,
lvl_h,
lvl_w,
pwc_h,
pwc_w,
lvl,
frameNum=5,
blur=False):
ratio_h = float(lvl_h) / float(pwc_h)
ratio_w = float(lvl_w) / float(pwc_w)
nn = ModelPWCNet(mode='test', options=nn_opts)
nn.print_config()
for i in range(frameNum):
B[i] = tf.image.resize_bilinear(B[i], (pwc_h, pwc_w),
align_corners=True)
tmp_list = []
for i in range(frameNum):
for j in range(frameNum):
tmp_list.append(tf.stack([B[i], B[j]], 1))
PWC_input = tf.concat(tmp_list, 0) # [batch_size*20, 2, H, W, 3]
PWC_input = tf.reshape(
PWC_input,
[FLAGS.batch_size * (frameNum * frameNum), 2, pwc_h, pwc_w, 3])
pred_labels, _ = nn.nn(PWC_input, reuse=tf.AUTO_REUSE)
print(pred_labels)
pred_labels = tf.image.resize_bilinear(pred_labels, (lvl_h, lvl_w),
align_corners=True)
"""
0: W
1: H
"""
ratio_tensor = tf.expand_dims(
tf.expand_dims(
tf.expand_dims(
tf.convert_to_tensor(np.asarray([ratio_w, ratio_h]),
dtype=tf.float32), 0), 0), 0)
FB = []
counter = 0
for i in range(frameNum):
FB_tmp = []
for j in range(frameNum):
FB_tmp.append(
tf.stop_gradient(pred_labels[FLAGS.batch_size *
counter:FLAGS.batch_size *
(counter + 1)] *
ratio_tensor))
counter += 1
FB.append(FB_tmp)
return FB
def PWC_full(F0, F1, F2, F3, F4, B0, B1, B2, B3, B4, lvl_h, lvl_w, pwc_h, pwc_w):
ratio_h = float(lvl_h) / float(pwc_h)
ratio_w = float(lvl_w) / float(pwc_w)
nn = ModelPWCNet(mode='test', options=nn_opts)
nn.print_config()
F0 = tf.image.resize_bilinear(F0, (pwc_h, pwc_w))
F1 = tf.image.resize_bilinear(F1, (pwc_h, pwc_w))
F2 = tf.image.resize_bilinear(F2, (pwc_h, pwc_w))
F3 = tf.image.resize_bilinear(F3, (pwc_h, pwc_w))
F4 = tf.image.resize_bilinear(F4, (pwc_h, pwc_w))
B0 = tf.image.resize_bilinear(B0, (pwc_h, pwc_w))
B1 = tf.image.resize_bilinear(B1, (pwc_h, pwc_w))
B2 = tf.image.resize_bilinear(B2, (pwc_h, pwc_w))
B3 = tf.image.resize_bilinear(B3, (pwc_h, pwc_w))
B4 = tf.image.resize_bilinear(B4, (pwc_h, pwc_w))
tmp_list = []
tmp_list.append(tf.stack([F0, F1], 1))
tmp_list.append(tf.stack([F0, F2], 1))
tmp_list.append(tf.stack([F0, F3], 1))
tmp_list.append(tf.stack([F0, F4], 1))
tmp_list.append(tf.stack([F1, F0], 1))
tmp_list.append(tf.stack([F1, F2], 1))
tmp_list.append(tf.stack([F1, F3], 1))
tmp_list.append(tf.stack([F1, F4], 1))
tmp_list.append(tf.stack([F2, F0], 1))
tmp_list.append(tf.stack([F2, F1], 1))
tmp_list.append(tf.stack([F2, F3], 1))
tmp_list.append(tf.stack([F2, F4], 1))
tmp_list.append(tf.stack([F3, F0], 1))
tmp_list.append(tf.stack([F3, F1], 1))
tmp_list.append(tf.stack([F3, F2], 1))
tmp_list.append(tf.stack([F3, F4], 1))
tmp_list.append(tf.stack([F4, F0], 1))
tmp_list.append(tf.stack([F4, F1], 1))
tmp_list.append(tf.stack([F4, F2], 1))
tmp_list.append(tf.stack([F4, F3], 1))
tmp_list.append(tf.stack([B0, B1], 1))
tmp_list.append(tf.stack([B0, B2], 1))
tmp_list.append(tf.stack([B0, B3], 1))
tmp_list.append(tf.stack([B0, B4], 1))
tmp_list.append(tf.stack([B1, B0], 1))
tmp_list.append(tf.stack([B1, B2], 1))
tmp_list.append(tf.stack([B1, B3], 1))
tmp_list.append(tf.stack([B1, B4], 1))
tmp_list.append(tf.stack([B2, B0], 1))
tmp_list.append(tf.stack([B2, B1], 1))
tmp_list.append(tf.stack([B2, B3], 1))
tmp_list.append(tf.stack([B2, B4], 1))
tmp_list.append(tf.stack([B3, B0], 1))
tmp_list.append(tf.stack([B3, B1], 1))
tmp_list.append(tf.stack([B3, B2], 1))
tmp_list.append(tf.stack([B3, B4], 1))
tmp_list.append(tf.stack([B4, B0], 1))
tmp_list.append(tf.stack([B4, B1], 1))
tmp_list.append(tf.stack([B4, B2], 1))
tmp_list.append(tf.stack([B4, B3], 1))
PWC_input = tf.concat(tmp_list, 0) # [batch_size*20, 2, H, W, 3]
PWC_input = tf.reshape(PWC_input, [FLAGS.batch_size * 40, 2, pwc_h, pwc_w, 3])
pred_labels, _ = nn.nn(PWC_input, reuse=tf.AUTO_REUSE)
print(pred_labels)
pred_labels = tf.image.resize_bilinear(pred_labels, (lvl_h, lvl_w))
"""
0: W
1: H
"""
ratio_tensor = tf.expand_dims(tf.expand_dims(tf.expand_dims(tf.convert_to_tensor(np.asarray([ratio_w, ratio_h]), dtype=tf.float32), 0), 0), 0)
FF01 = pred_labels[FLAGS.batch_size * 0:FLAGS.batch_size * 1] * ratio_tensor
FF02 = pred_labels[FLAGS.batch_size * 1:FLAGS.batch_size * 2] * ratio_tensor
FF03 = pred_labels[FLAGS.batch_size * 2:FLAGS.batch_size * 3] * ratio_tensor
FF04 = pred_labels[FLAGS.batch_size * 3:FLAGS.batch_size * 4] * ratio_tensor
FF10 = pred_labels[FLAGS.batch_size * 4:FLAGS.batch_size * 5] * ratio_tensor
FF12 = pred_labels[FLAGS.batch_size * 5:FLAGS.batch_size * 6] * ratio_tensor
FF13 = pred_labels[FLAGS.batch_size * 6:FLAGS.batch_size * 7] * ratio_tensor
FF14 = pred_labels[FLAGS.batch_size * 7:FLAGS.batch_size * 8] * ratio_tensor
FF20 = pred_labels[FLAGS.batch_size * 8:FLAGS.batch_size * 9] * ratio_tensor
FF21 = pred_labels[FLAGS.batch_size * 9:FLAGS.batch_size * 10] * ratio_tensor
FF23 = pred_labels[FLAGS.batch_size * 10:FLAGS.batch_size * 11] * ratio_tensor
FF24 = pred_labels[FLAGS.batch_size * 11:FLAGS.batch_size * 12] * ratio_tensor
FF30 = pred_labels[FLAGS.batch_size * 12:FLAGS.batch_size * 13] * ratio_tensor
FF31 = pred_labels[FLAGS.batch_size * 13:FLAGS.batch_size * 14] * ratio_tensor
FF32 = pred_labels[FLAGS.batch_size * 14:FLAGS.batch_size * 15] * ratio_tensor
FF34 = pred_labels[FLAGS.batch_size * 15:FLAGS.batch_size * 16] * ratio_tensor
FF40 = pred_labels[FLAGS.batch_size * 16:FLAGS.batch_size * 17] * ratio_tensor
FF41 = pred_labels[FLAGS.batch_size * 17:FLAGS.batch_size * 18] * ratio_tensor
FF42 = pred_labels[FLAGS.batch_size * 18:FLAGS.batch_size * 19] * ratio_tensor
FF43 = pred_labels[FLAGS.batch_size * 19:FLAGS.batch_size * 20] * ratio_tensor
FB01 = pred_labels[FLAGS.batch_size * 20:FLAGS.batch_size * 21] * ratio_tensor
FB02 = pred_labels[FLAGS.batch_size * 21:FLAGS.batch_size * 22] * ratio_tensor
FB03 = pred_labels[FLAGS.batch_size * 22:FLAGS.batch_size * 23] * ratio_tensor
FB04 = pred_labels[FLAGS.batch_size * 23:FLAGS.batch_size * 24] * ratio_tensor
FB10 = pred_labels[FLAGS.batch_size * 24:FLAGS.batch_size * 25] * ratio_tensor
FB12 = pred_labels[FLAGS.batch_size * 25:FLAGS.batch_size * 26] * ratio_tensor
FB13 = pred_labels[FLAGS.batch_size * 26:FLAGS.batch_size * 27] * ratio_tensor
FB14 = pred_labels[FLAGS.batch_size * 27:FLAGS.batch_size * 28] * ratio_tensor
FB20 = pred_labels[FLAGS.batch_size * 28:FLAGS.batch_size * 29] * ratio_tensor
FB21 = pred_labels[FLAGS.batch_size * 29:FLAGS.batch_size * 30] * ratio_tensor
FB23 = pred_labels[FLAGS.batch_size * 30:FLAGS.batch_size * 31] * ratio_tensor
FB24 = pred_labels[FLAGS.batch_size * 31:FLAGS.batch_size * 32] * ratio_tensor
FB30 = pred_labels[FLAGS.batch_size * 32:FLAGS.batch_size * 33] * ratio_tensor
FB31 = pred_labels[FLAGS.batch_size * 33:FLAGS.batch_size * 34] * ratio_tensor
FB32 = pred_labels[FLAGS.batch_size * 34:FLAGS.batch_size * 35] * ratio_tensor
FB34 = pred_labels[FLAGS.batch_size * 35:FLAGS.batch_size * 36] * ratio_tensor
FB40 = pred_labels[FLAGS.batch_size * 36:FLAGS.batch_size * 37] * ratio_tensor
FB41 = pred_labels[FLAGS.batch_size * 37:FLAGS.batch_size * 38] * ratio_tensor
FB42 = pred_labels[FLAGS.batch_size * 38:FLAGS.batch_size * 39] * ratio_tensor
FB43 = pred_labels[FLAGS.batch_size * 39:FLAGS.batch_size * 40] * ratio_tensor
return FF01, FF02, FF03, FF04, \
FF10, FF12, FF13, FF14, \
FF20, FF21, FF23, FF24, \
FF30, FF31, FF32, FF34, \
FF40, FF41, FF42, FF43, \
FB01, FB02, FB03, FB04, \
FB10, FB12, FB13, FB14, \
FB20, FB21, FB23, FB24, \
FB30, FB31, FB32, FB34, \
FB40, FB41, FB42, FB43
B3_pred_0_down = tf.image.resize_bilinear(apply_gaussian_blur_image(B3_pred_0), (pwc_h, pwc_w), align_corners=True)
B4_pred_0_down = tf.image.resize_bilinear(apply_gaussian_blur_image(B4_pred_0), (pwc_h, pwc_w), align_corners=True)
ratio_h = float(CROP_PATCH_H // (2 ** down_level)) / float(pwc_h)
ratio_w = float(CROP_PATCH_W // (2 ** down_level)) / float(pwc_w)
nn = ModelPWCNet(mode='test', options=nn_opts)
nn.print_config()
tmp_list = []
tmp_list.append(tf.stack([B2_pred_0_down, B0_pred_0_down], 1))
tmp_list.append(tf.stack([B2_pred_0_down, B1_pred_0_down], 1))
tmp_list.append(tf.stack([B2_pred_0_down, B3_pred_0_down], 1))
tmp_list.append(tf.stack([B2_pred_0_down, B4_pred_0_down], 1))
PWC_input = tf.concat(tmp_list, 0) # [batch_size*20, 2, H, W, 3]
PWC_input = tf.reshape(PWC_input, [FLAGS.batch_size * 4, 2, pwc_h, pwc_w, 3])
pred_labels, _ = nn.nn(PWC_input, reuse=tf.AUTO_REUSE)
pred_labels = tf.image.resize_bilinear(pred_labels, (RESIZED_H // (2 ** down_level), RESIZED_W // (2 ** down_level)), align_corners=True)
"""
0: W
1: H
"""
ratio_tensor = tf.expand_dims(tf.expand_dims(tf.expand_dims(tf.convert_to_tensor(np.asarray([ratio_w, ratio_h]), dtype=tf.float32), 0), 0), 0)
FB20 = pred_labels[FLAGS.batch_size * 0:FLAGS.batch_size * 1] * ratio_tensor * (2 ** down_level)
FB21 = pred_labels[FLAGS.batch_size * 1:FLAGS.batch_size * 2] * ratio_tensor * (2 ** down_level)
FB23 = pred_labels[FLAGS.batch_size * 2:FLAGS.batch_size * 3] * ratio_tensor * (2 ** down_level)
FB24 = pred_labels[FLAGS.batch_size * 3:FLAGS.batch_size * 4] * ratio_tensor * (2 ** down_level)
FB20 = tf.image.resize_bilinear(FB20, [RESIZED_H, RESIZED_W], align_corners=True)
FB21 = tf.image.resize_bilinear(FB21, [RESIZED_H, RESIZED_W], align_corners=True)
FB23 = tf.image.resize_bilinear(FB23, [RESIZED_H, RESIZED_W], align_corners=True)
FB24 = tf.image.resize_bilinear(FB24, [RESIZED_H, RESIZED_W], align_corners=True)